In [1]:
import json
import copy
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
from torchsummary import summary
In [2]:
from nmfd_gnn import NMFD_GNN

1: set parameters¶

In [3]:
print (torch.cuda.is_available())
device = torch.device("cuda:0")
random_seed = 42
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
r = random.random
True
In [4]:
#1.1: settings
M = 10                       #number of time interval in a window
missing_ratio = 0.50
file_name = "m_" + str(M) + "_missing_" + str(int(missing_ratio*100))
print (file_name)

#1.2: hyperparameters
num_epochs, batch_size, learning_rate = 200, 16, 0.001
beta_flow, beta_occ, beta_phy = 1.0, 1.0, 0.1
batch_size_vt = 16  #batch size for evaluation and test

hyper = {"n_e": num_epochs, "b_s": batch_size, "b_s_vt": batch_size_vt, "l_r": learning_rate,\
         "beta_f": beta_flow, "beta_o": beta_occ, "beta_p": beta_phy}

gnn_dim_1, gnn_dim_2, gnn_dim_3, lstm_dim = 2, 128, 128, 128
p_dim = 10    #column dimension of L1, L2
c_k = 5.5     #meter, the sum of loop width and uniform vehicle length. based on Gero and Daganzo 2008.
theta_ini = [-2.879, 5.207, -2.473, 1.722, 3.619] 

hyper_model = {"g_dim_1": gnn_dim_1, "g_dim_2": gnn_dim_2, "g_dim_3": gnn_dim_3, "l_dim": lstm_dim,\
               "p_dim": p_dim, "c_k": c_k, "theta_ini": theta_ini}
max_no_decrease = 30

#1.3: set paths
root_path = "/home/umni2/a/umnilab/users/xue120/umni4/2023_mfd_traffic_london/"
file_path = root_path + "2_prepare_data/" + file_name + "/"
train_path, vali_path, test_path =\
    file_path + "train.json", file_path + "vali.json", file_path + "test.json"
sensor_id_path = file_path + "sensor_id_order.json"
sensor_adj_path = file_path + "sensor_adj.json"
mean_std_path = file_path + "mean_std.json"
m_10_missing_50

2: visualization¶

In [5]:
def visualize_train_loss(total_phy_flow_occ_loss):
    plt.figure(figsize=(4,3), dpi=75)
    t_p_f_o_l = np.array(total_phy_flow_occ_loss)
    e_loss, p_loss, f_loss, o_loss = t_p_f_o_l[:,0], t_p_f_o_l[:,1], t_p_f_o_l[:,2], t_p_f_o_l[:,3]
    x = range(len(e_loss))
    plt.plot(x, p_loss, linewidth=1, label = "phy loss")
    plt.plot(x, f_loss, linewidth=1, label = "flow loss")
    plt.plot(x, o_loss, linewidth=1, label = "occ loss")
    plt.legend()
    plt.title('Loss decline on train')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.savefig(file_name + '/' + 'train_loss.png', bbox_inches = 'tight')
    plt.show()
    
def visualize_flow_loss(vali_f_mae, test_f_mae):
    plt.figure(figsize=(4,3), dpi=75)
    x = range(len(vali_f_mae))    
    plt.plot(x, vali_f_mae, linewidth=1, label="Validate")
    plt.plot(x, test_f_mae, linewidth=1, label="Test")
    plt.legend()
    plt.title('MAE of flow on validate/test')
    plt.xlabel('Epoch')
    plt.ylabel('MAE (veh/h)')
    plt.savefig(file_name + '/' + 'flow_mae.png', bbox_inches = 'tight')
    plt.show()
    
def visualize_occ_loss(vali_o_mae, test_o_mae):
    plt.figure(figsize=(4,3), dpi=75)
    x = range(len(vali_o_mae))    
    plt.plot(x, vali_o_mae, linewidth=1, label="Validate")
    plt.plot(x, test_o_mae, linewidth=1, label="Test")
    plt.legend()
    plt.title('MAE of occupancy on validate/test')
    plt.xlabel('Epoch')
    plt.ylabel('MAE')
    plt.savefig(file_name + '/' + 'occ_mae.png',bbox_inches = 'tight')
    plt.show()

3: compute the error¶

In [6]:
def MAELoss(yhat, y):
    return float(torch.mean(torch.div(torch.abs(yhat-y), 1)))

def RMSELoss(yhat, y):
    return float(torch.sqrt(torch.mean((yhat-y)**2)))

def vali_test(model, f, f_mask, o, o_mask, f_o_mean_std, b_s_vt):    
    flow_std, occ_std, n = f_o_mean_std[1], f_o_mean_std[3], len(f)
    f_mae_list, f_rmse_list, o_mae_list, o_rmse_list, num_list = list(), list(), list(), list(), list()
    for i in range(0, n, b_s_vt):
        s, e = i, np.min([i+b_s_vt, n])
        num_list.append(e-s)
        bf, bo, bf_mask, bo_mask = f[s: e], o[s: e], f_mask[s: e], o_mask[s: e]  
        bf_hat, bo_hat, bq_hat, bq_theta = model.run(bf_mask, bo_mask)
        bf_hat, bo_hat = bf_hat.cpu(), bo_hat.cpu()
        bf_mae, bf_rmse = MAELoss(bf_hat, bf)*flow_std, RMSELoss(bf_hat, bf)*flow_std
        bo_mae, bo_rmse = MAELoss(bo_hat, bo)*occ_std, RMSELoss(bo_hat, bo)*occ_std
        f_mae_list.append(bf_mae)
        f_rmse_list.append(bf_rmse)
        o_mae_list.append(bo_mae)
        o_rmse_list.append(bo_rmse)
    f_mae, o_mae = np.dot(f_mae_list, num_list)/n, np.dot(o_mae_list, num_list)/n
    f_rmse = np.sqrt(np.dot(np.multiply(f_rmse_list, f_rmse_list), num_list)/n)
    o_rmse = np.sqrt(np.dot(np.multiply(o_rmse_list, o_rmse_list), num_list)/n)
    return f_mae, f_rmse, o_mae, o_rmse

def evaluate(model, vt_f, vt_o, vt_f_m, vt_o_m, f_o_mean_std, b_s_vt): #vt: vali_test
    vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse  =\
         vali_test(model, vt_f, vt_f_m, vt_o, vt_o_m, f_o_mean_std, b_s_vt)
    return vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse

4: train¶

In [7]:
#4.1: one training epoch
def train_epoch(model, opt, criterion, train_f_x, train_f_y, train_o_x, train_o_y, hyper, flow_std_squ): 
    #f: flow; o: occupancy
    model.train()
    losses, p_losses, f_losses, o_losses = list(), list(), list(), list()
    
    beta_f, beta_o, beta_p, b_s = hyper["beta_f"], hyper["beta_o"], hyper["beta_p"], hyper["b_s"]
    n = len(train_f_x)
    print ("# batch: ", int(n/b_s))   
    
    for i in range(0, n-b_s, b_s):
        time1 = time.time()
        x_f_batch, y_f_batch = train_f_x[i: i+b_s], train_f_y[i: i+b_s]   
        x_o_batch, y_o_batch = train_o_x[i: i+b_s], train_o_y[i: i+b_s]

        opt.zero_grad() 
        y_f_hat, y_o_hat, q_hat, q_theta = model.run(x_f_batch, x_o_batch)
        
        p_loss = criterion(q_hat, q_theta).cpu()                #physical loss 
        p_loss = p_loss/flow_std_squ
        
        f_loss = criterion(y_f_hat.cpu(), y_f_batch)              #data loss of flow
        o_loss = criterion(y_o_hat.cpu(), y_o_batch)              #data loss of occupancy
        
        loss = beta_f*f_loss + beta_o*o_loss + beta_p*p_loss
        
        loss.backward()
        opt.step()
        losses.append(loss.data.numpy())
        p_losses.append(p_loss.data.numpy())
        f_losses.append(f_loss.data.numpy())
        o_losses.append(o_loss.data.numpy())
        
        if i % (64*b_s) == 0:
            print ("i_batch: ", i/b_s)
            print ("the loss for this batch: ", loss.data.numpy())
            print ("flow loss", f_loss.data.numpy())
            print ("occ loss", o_loss.data.numpy())
            time2 = time.time()
            print ("time for this batch", time2-time1)
            print ("----------------------------------")
        n_loss = float(len(losses)+0.000001)
        aver_loss = sum(losses)/n_loss
        aver_p_loss = sum(p_losses)/n_loss
        aver_f_loss = sum(f_losses)/n_loss
        aver_o_loss = sum(o_losses)/n_loss
    return aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss

#4.2: all train epochs
def train_process(model, criterion, train, vali, test, hyper, f_o_mean_std):
    total_phy_flow_occ_loss = list()
    
    n_mse_flow_occ = 0 #mse(flow) + mse(occ) for validation sets.
    
    vali_f, vali_o = vali["flow"], vali["occupancy"] 
    vali_f_m, vali_o_m = vali["flow_mask"].to(device), vali["occupancy_mask"].to(device) 
    test_f, test_o = test["flow"], test["occupancy"] 
    test_f_m, test_o_m = test["flow_mask"].to(device), test["occupancy_mask"].to(device) 
    
    l_r, n_e = hyper["l_r"], hyper["n_e"]
    opt = optim.Adam(model.parameters(), l_r, betas = (0.9,0.999), weight_decay=0.0001)
    opt_scheduler = torch.optim.lr_scheduler.MultiStepLR(opt, milestones=[150])
    
    print ("# epochs ", n_e)
    r_vali_f_mae, r_vali_o_mae, r_test_f_mae, r_test_o_mae = list(), list(), list(), list()
    r_vali_f_rmse, r_vali_o_rmse, r_test_f_rmse, r_test_o_rmse = list(), list(), list(), list()
    
    flow_std_squ = np.power(f_o_mean_std[1], 2)
    
    no_decrease = 0
    for i in range(n_e):
        print ("----------------an epoch starts-------------------")
        #time1_s = time.time()
        
        time_s = time.time()
        print ("i_epoch: ", i)
        n_train = len(train["flow"])
        number_list = copy.copy(list(range(n_train)))
        random.shuffle(number_list, random = r)
        shuffle_idx = torch.tensor(number_list)
        train_x_f, train_y_f = train["flow_mask"][shuffle_idx], train["flow"][shuffle_idx]
        train_x_o, train_y_o = train["occupancy_mask"][shuffle_idx], train["occupancy"][shuffle_idx] 
        
        
        aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss =\
            train_epoch(model, opt, criterion, train_x_f.to(device), train_y_f,\
                        train_x_o.to(device), train_y_o, hyper, flow_std_squ)
        opt_scheduler.step()
        
        total_phy_flow_occ_loss.append([aver_loss, aver_p_loss, aver_f_loss, aver_o_loss])
        print ("train loss for this epoch: ", round(aver_loss, 6))
        
        #evaluate
        b_s_vt = hyper["b_s_vt"]
        vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
            evaluate(model, vali_f, vali_o, vali_f_m, vali_o_m, f_o_mean_std, b_s_vt)
        test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
            evaluate(model, test_f, test_o, test_f_m, test_o_m, f_o_mean_std, b_s_vt)  
        
        r_vali_f_mae.append(vali_f_mae)
        r_test_f_mae.append(test_f_mae)
        r_vali_o_mae.append(vali_o_mae)
        r_test_o_mae.append(test_o_mae)
        r_vali_f_rmse.append(vali_f_rmse)
        r_test_f_rmse.append(test_f_rmse)
        r_vali_o_rmse.append(vali_o_rmse)
        r_test_o_rmse.append(test_o_rmse)
        
        visualize_train_loss(total_phy_flow_occ_loss)
        visualize_flow_loss(r_vali_f_mae, r_test_f_mae)
        visualize_occ_loss(r_vali_o_mae, r_test_o_mae)
        time_e = time.time()
        print ("time for this epoch", time_e - time_s)
        
        performance = {"train": total_phy_flow_occ_loss,\
                  "vali": [r_vali_f_mae, r_vali_f_rmse, r_vali_o_mae, r_vali_o_rmse],\
                  "test": [r_test_f_mae, r_test_f_rmse, r_test_o_mae, r_test_o_rmse]}
        subfile =  open(file_name + '/' + 'performance'+'.json','w')
        json.dump(performance, subfile)
        subfile.close()
        
        #early stop
        flow_std, occ_std = f_o_mean_std[1], f_o_mean_std[3]
        norm_f_rmse, norm_o_rmse = vali_f_rmse/flow_std, vali_o_rmse/occ_std
        norm_sum_mse = norm_f_rmse*norm_f_rmse + norm_o_rmse*norm_o_rmse
        
        if n_mse_flow_occ > 0:
            min_until_now = min([min_until_now, norm_sum_mse])
        else:
            min_until_now = 1000000.0  
        if norm_sum_mse > min_until_now:
            no_decrease = no_decrease+1
        else:
            no_decrease = 0
        if no_decrease == max_no_decrease:
            print ("Early stop at the " + str(i+1) + "-th epoch")
            return total_phy_flow_occ_loss, model 
        n_mse_flow_occ = n_mse_flow_occ + 1
        
        print ("No_decrease: ", no_decrease)
    return total_phy_flow_occ_loss, model    

5: prepare tensors¶

In [8]:
def tensorize(train_vali_test):
    result = dict()
    result["flow"] = torch.tensor(train_vali_test["flow"]) 
    result["flow_mask"] = torch.tensor(train_vali_test["flow_mask"])     
    result["occupancy"] = torch.tensor(train_vali_test["occupancy"]) 
    result["occupancy_mask"] = torch.tensor(train_vali_test["occupancy_mask"]) 
    return result

def normalize_flow_occ(tvt, f_o_mean_std):  #tvt: train, vali, test
    #flow
    f_mean, f_std = f_o_mean_std[0], f_o_mean_std[1]
    f_mask, f = tvt["flow_mask"], tvt["flow"]
    tvt["flow_mask"] = ((np.array(f_mask)-f_mean)/f_std).tolist()
    tvt["flow"] = ((np.array(f)-f_mean)/f_std).tolist()
    
    #occ
    o_mean, o_std = f_o_mean_std[2], f_o_mean_std[3]
    o_mask, o = tvt["occupancy_mask"], tvt["occupancy"]
    tvt["occupancy_mask"] = ((np.array(o_mask)-o_mean)/o_std).tolist()
    tvt["occupancy"] = ((np.array(o)-o_mean)/o_std).tolist()   
    return tvt

def transform_distance(d_matrix):
    sigma, n_row, n_col = np.std(d_matrix), len(d_matrix), len(d_matrix[0])
    sigma_square = sigma*sigma
    for i in range(n_row):
        for j in range(n_col):
            d_i_j = d_matrix[i][j]
            d_matrix[i][j] = np.exp(0.0-10000.0*d_i_j*d_i_j/sigma_square)
    return d_matrix

def load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path):
    mean_std = json.load(open(mean_std_path))
    f_mean, f_std, o_mean, o_std =\
        mean_std["f_mean"], mean_std["f_std"], mean_std["o_mean"], mean_std["o_std"]
    f_o_mean_std = [f_mean, f_std, o_mean, o_std]
    
    train = json.load(open(train_path))
    vali = json.load(open(vali_path))
    test = json.load(open(test_path))
    adj = json.load(open(sensor_adj_path))["adj"]
    n_sensor = len(train["flow"][0])    
    
    train = tensorize(normalize_flow_occ(train, f_o_mean_std))
    vali = tensorize(normalize_flow_occ(vali, f_o_mean_std))
    test = tensorize(normalize_flow_occ(test, f_o_mean_std))

    adj = torch.tensor(transform_distance(adj), device=device).float()   
    
    df_sensor_id = json.load(open(sensor_id_path))
    sensor_length = [0.0 for i in range(n_sensor)]
    for sensor in df_sensor_id:
        sensor_length[df_sensor_id[sensor][0]] = df_sensor_id[sensor][3]
        
    return train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length

6: main¶

In [9]:
#6.1 load the data
time1 = time.time()
train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length =\
    load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path)
time2 = time.time()
print (time2-time1)
9.420258522033691
In [10]:
print (len(train["flow"]))
print (len(vali["flow"]))
print (len(test["flow"]))
print (f_o_mean_std)
1546
509
510
[426.01546608926594, 254.82043085525967, 0.1815290985289925, 0.18313943695883658]
In [11]:
model = NMFD_GNN(n_sensor, M, hyper_model, f_o_mean_std, sensor_length, adj).to(device)   
cri = nn.MSELoss() 
In [12]:
#6.2: train the model
total_phy_flow_occ_loss, trained_model = train_process(model, cri, train, vali, test, hyper, f_o_mean_std)
# epochs  200
----------------an epoch starts-------------------
i_epoch:  0
# batch:  96
i_batch:  0.0
the loss for this batch:  1.756126
flow loss 0.96840364
occ loss 0.5164554
time for this batch 0.6292071342468262
----------------------------------
i_batch:  64.0
the loss for this batch:  0.7691758
flow loss 0.32693413
occ loss 0.24356198
time for this batch 0.30269742012023926
----------------------------------
train loss for this epoch:  0.916269
time for this epoch 35.5462920665741
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  1
# batch:  96
i_batch:  0.0
the loss for this batch:  0.7902627
flow loss 0.2270321
occ loss 0.29525086
time for this batch 0.2667880058288574
----------------------------------
i_batch:  64.0
the loss for this batch:  0.765943
flow loss 0.25759965
occ loss 0.23616791
time for this batch 0.30664849281311035
----------------------------------
train loss for this epoch:  0.608924
time for this epoch 35.368701696395874
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  2
# batch:  96
i_batch:  0.0
the loss for this batch:  0.64471906
flow loss 0.24113876
occ loss 0.19630018
time for this batch 0.2648639678955078
----------------------------------
i_batch:  64.0
the loss for this batch:  0.5223365
flow loss 0.16252653
occ loss 0.15570612
time for this batch 0.28449010848999023
----------------------------------
train loss for this epoch:  0.557005
time for this epoch 37.049219846725464
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  3
# batch:  96
i_batch:  0.0
the loss for this batch:  0.64824986
flow loss 0.17933199
occ loss 0.22120753
time for this batch 0.2592496871948242
----------------------------------
i_batch:  64.0
the loss for this batch:  0.4531036
flow loss 0.14424282
occ loss 0.11786274
time for this batch 0.28776979446411133
----------------------------------
train loss for this epoch:  0.532895
time for this epoch 36.144810914993286
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  4
# batch:  96
i_batch:  0.0
the loss for this batch:  0.55143195
flow loss 0.15508667
occ loss 0.17111066
time for this batch 0.24134159088134766
----------------------------------
i_batch:  64.0
the loss for this batch:  0.5244114
flow loss 0.16505179
occ loss 0.15792157
time for this batch 0.29525279998779297
----------------------------------
train loss for this epoch:  0.519848
time for this epoch 35.361496448516846
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  5
# batch:  96
i_batch:  0.0
the loss for this batch:  0.5039437
flow loss 0.2195823
occ loss 0.13965668
time for this batch 0.24945402145385742
----------------------------------
i_batch:  64.0
the loss for this batch:  0.4976476
flow loss 0.16113158
occ loss 0.1368231
time for this batch 0.2884237766265869
----------------------------------
train loss for this epoch:  0.506634
time for this epoch 36.22963738441467
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  6
# batch:  96
i_batch:  0.0
the loss for this batch:  0.47319472
flow loss 0.14466958
occ loss 0.1389649
time for this batch 0.263427734375
----------------------------------
i_batch:  64.0
the loss for this batch:  0.526335
flow loss 0.16819023
occ loss 0.15133987
time for this batch 0.2713165283203125
----------------------------------
train loss for this epoch:  0.498644
time for this epoch 36.58422923088074
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  7
# batch:  96
i_batch:  0.0
the loss for this batch:  0.4305077
flow loss 0.1474947
occ loss 0.11070592
time for this batch 0.25819969177246094
----------------------------------
i_batch:  64.0
the loss for this batch:  0.49361482
flow loss 0.13482773
occ loss 0.12745188
time for this batch 0.3046131134033203
----------------------------------
train loss for this epoch:  0.491752
time for this epoch 35.9535698890686
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  8
# batch:  96
i_batch:  0.0
the loss for this batch:  0.44863284
flow loss 0.15559214
occ loss 0.13048545
time for this batch 0.27020716667175293
----------------------------------
i_batch:  64.0
the loss for this batch:  0.49166107
flow loss 0.1409293
occ loss 0.14844431
time for this batch 0.26935744285583496
----------------------------------
train loss for this epoch:  0.480863
time for this epoch 35.105350732803345
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  9
# batch:  96
i_batch:  0.0
the loss for this batch:  0.41845298
flow loss 0.13154964
occ loss 0.117339276
time for this batch 0.2629361152648926
----------------------------------
i_batch:  64.0
the loss for this batch:  0.47036994
flow loss 0.13332112
occ loss 0.12250854
time for this batch 0.2754251956939697
----------------------------------
train loss for this epoch:  0.472906
time for this epoch 35.75370740890503
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  10
# batch:  96
i_batch:  0.0
the loss for this batch:  0.5530465
flow loss 0.1433666
occ loss 0.17468971
time for this batch 0.25672149658203125
----------------------------------
i_batch:  64.0
the loss for this batch:  0.47371632
flow loss 0.14723374
occ loss 0.12909494
time for this batch 0.27674245834350586
----------------------------------
train loss for this epoch:  0.464616
time for this epoch 35.85735368728638
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  11
# batch:  96
i_batch:  0.0
the loss for this batch:  0.47892523
flow loss 0.119601615
occ loss 0.16920745
time for this batch 0.2329695224761963
----------------------------------
i_batch:  64.0
the loss for this batch:  0.44641173
flow loss 0.13179874
occ loss 0.11547644
time for this batch 0.29494142532348633
----------------------------------
train loss for this epoch:  0.462981
time for this epoch 36.424917221069336
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  12
# batch:  96
i_batch:  0.0
the loss for this batch:  0.45429796
flow loss 0.12543762
occ loss 0.13771133
time for this batch 0.2579801082611084
----------------------------------
i_batch:  64.0
the loss for this batch:  0.4139549
flow loss 0.10663282
occ loss 0.09882314
time for this batch 0.3045318126678467
----------------------------------
train loss for this epoch:  0.455342
time for this epoch 35.897597551345825
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  13
# batch:  96
i_batch:  0.0
the loss for this batch:  0.39583832
flow loss 0.11436169
occ loss 0.1002083
time for this batch 0.253969669342041
----------------------------------
i_batch:  64.0
the loss for this batch:  0.49565125
flow loss 0.12979513
occ loss 0.15565854
time for this batch 0.2619469165802002
----------------------------------
train loss for this epoch:  0.452026
time for this epoch 34.19408988952637
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  14
# batch:  96
i_batch:  0.0
the loss for this batch:  0.40815654
flow loss 0.10774005
occ loss 0.10972221
time for this batch 0.24858450889587402
----------------------------------
i_batch:  64.0
the loss for this batch:  0.5052139
flow loss 0.12485455
occ loss 0.14041676
time for this batch 0.2115175724029541
----------------------------------
train loss for this epoch:  0.449367
time for this epoch 34.89238882064819
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  15
# batch:  96
i_batch:  0.0
the loss for this batch:  0.37364072
flow loss 0.09824198
occ loss 0.09738622
time for this batch 0.2611970901489258
----------------------------------
i_batch:  64.0
the loss for this batch:  0.4337275
flow loss 0.10734318
occ loss 0.1391923
time for this batch 0.29834747314453125
----------------------------------
train loss for this epoch:  0.445356
time for this epoch 35.65042281150818
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  16
# batch:  96
i_batch:  0.0
the loss for this batch:  0.446905
flow loss 0.09882761
occ loss 0.14163142
time for this batch 0.23355698585510254
----------------------------------
i_batch:  64.0
the loss for this batch:  0.42581922
flow loss 0.1347997
occ loss 0.117331564
time for this batch 0.2479712963104248
----------------------------------
train loss for this epoch:  0.445664
time for this epoch 33.41999697685242
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  17
# batch:  96
i_batch:  0.0
the loss for this batch:  0.45788777
flow loss 0.10802805
occ loss 0.13813433
time for this batch 0.25753092765808105
----------------------------------
i_batch:  64.0
the loss for this batch:  0.39387184
flow loss 0.113043025
occ loss 0.10760001
time for this batch 0.28408336639404297
----------------------------------
train loss for this epoch:  0.441337
time for this epoch 35.7675986289978
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  18
# batch:  96
i_batch:  0.0
the loss for this batch:  0.38283712
flow loss 0.10173752
occ loss 0.112210825
time for this batch 0.24501371383666992
----------------------------------
i_batch:  64.0
the loss for this batch:  0.38801086
flow loss 0.10143746
occ loss 0.10010615
time for this batch 0.29836559295654297
----------------------------------
train loss for this epoch:  0.440208
time for this epoch 36.280253887176514
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  19
# batch:  96
i_batch:  0.0
the loss for this batch:  0.4913326
flow loss 0.12420317
occ loss 0.1504476
time for this batch 0.25475478172302246
----------------------------------
i_batch:  64.0
the loss for this batch:  0.5044532
flow loss 0.12461852
occ loss 0.1410854
time for this batch 0.2899470329284668
----------------------------------
train loss for this epoch:  0.439968
time for this epoch 35.2817759513855
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  20
# batch:  96
i_batch:  0.0
the loss for this batch:  0.41618297
flow loss 0.11357675
occ loss 0.10196097
time for this batch 0.2518939971923828
----------------------------------
i_batch:  64.0
the loss for this batch:  0.3565154
flow loss 0.10502416
occ loss 0.08164093
time for this batch 0.31593942642211914
----------------------------------
train loss for this epoch:  0.43553
time for this epoch 35.86111497879028
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  21
# batch:  96
i_batch:  0.0
the loss for this batch:  0.46417153
flow loss 0.10817044
occ loss 0.14338371
time for this batch 0.2766733169555664
----------------------------------
i_batch:  64.0
the loss for this batch:  0.43164778
flow loss 0.115751095
occ loss 0.10615767
time for this batch 0.2970426082611084
----------------------------------
train loss for this epoch:  0.434603
time for this epoch 34.591973066329956
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  22
# batch:  96
i_batch:  0.0
the loss for this batch:  0.47544318
flow loss 0.13187912
occ loss 0.13644698
time for this batch 0.2674679756164551
----------------------------------
i_batch:  64.0
the loss for this batch:  0.4482858
flow loss 0.11385987
occ loss 0.14300552
time for this batch 0.30493879318237305
----------------------------------
train loss for this epoch:  0.435775
time for this epoch 36.17198634147644
No_decrease:  7
----------------an epoch starts-------------------
i_epoch:  23
# batch:  96
i_batch:  0.0
the loss for this batch:  0.47184122
flow loss 0.11820487
occ loss 0.1478512
time for this batch 0.2584824562072754
----------------------------------
i_batch:  64.0
the loss for this batch:  0.5118351
flow loss 0.12984437
occ loss 0.15313245
time for this batch 0.30138564109802246
----------------------------------
train loss for this epoch:  0.432661
time for this epoch 36.07411980628967
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  24
# batch:  96
i_batch:  0.0
the loss for this batch:  0.44047296
flow loss 0.09795288
occ loss 0.121616
time for this batch 0.2489626407623291
----------------------------------
i_batch:  64.0
the loss for this batch:  0.48162097
flow loss 0.12431762
occ loss 0.13717936
time for this batch 0.314716100692749
----------------------------------
train loss for this epoch:  0.432456
time for this epoch 36.10537242889404
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  25
# batch:  96
i_batch:  0.0
the loss for this batch:  0.46044308
flow loss 0.114021026
occ loss 0.12224865
time for this batch 0.2643699645996094
----------------------------------
i_batch:  64.0
the loss for this batch:  0.44729704
flow loss 0.10871518
occ loss 0.12713906
time for this batch 0.2772181034088135
----------------------------------
train loss for this epoch:  0.42887
time for this epoch 35.97198128700256
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  26
# batch:  96
i_batch:  0.0
the loss for this batch:  0.41919953
flow loss 0.13933146
occ loss 0.10726908
time for this batch 0.2653377056121826
----------------------------------
i_batch:  64.0
the loss for this batch:  0.520895
flow loss 0.11200194
occ loss 0.16977812
time for this batch 0.30445075035095215
----------------------------------
train loss for this epoch:  0.432777
time for this epoch 36.331488370895386
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  27
# batch:  96
i_batch:  0.0
the loss for this batch:  0.3975486
flow loss 0.08314574
occ loss 0.11044614
time for this batch 0.26924705505371094
----------------------------------
i_batch:  64.0
the loss for this batch:  0.3469854
flow loss 0.10893553
occ loss 0.08882846
time for this batch 0.31287670135498047
----------------------------------
train loss for this epoch:  0.427952
time for this epoch 36.59539437294006
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  28
# batch:  96
i_batch:  0.0
the loss for this batch:  0.41709697
flow loss 0.10252827
occ loss 0.105297
time for this batch 0.24927115440368652
----------------------------------
i_batch:  64.0
the loss for this batch:  0.42741072
flow loss 0.11042383
occ loss 0.13674329
time for this batch 0.2944679260253906
----------------------------------
train loss for this epoch:  0.426581
time for this epoch 35.88730502128601
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  29
# batch:  96
i_batch:  0.0
the loss for this batch:  0.4363548
flow loss 0.09583878
occ loss 0.13200848
time for this batch 0.23067784309387207
----------------------------------
i_batch:  64.0
the loss for this batch:  0.4520461
flow loss 0.098678656
occ loss 0.12421869
time for this batch 0.29360175132751465
----------------------------------
train loss for this epoch:  0.424721
time for this epoch 36.42902588844299
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  30
# batch:  96
i_batch:  0.0
the loss for this batch:  0.37328625
flow loss 0.095999345
occ loss 0.08981567
time for this batch 0.22563433647155762
----------------------------------
i_batch:  64.0
the loss for this batch:  0.50733775
flow loss 0.12504162
occ loss 0.15161136
time for this batch 0.29054975509643555
----------------------------------
train loss for this epoch:  0.423945
time for this epoch 34.92092823982239
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  31
# batch:  96
i_batch:  0.0
the loss for this batch:  0.455963
flow loss 0.10707607
occ loss 0.13429768
time for this batch 0.2549893856048584
----------------------------------
i_batch:  64.0
the loss for this batch:  0.47897613
flow loss 0.11291302
occ loss 0.15254872
time for this batch 0.2838146686553955
----------------------------------
train loss for this epoch:  0.42242
time for this epoch 35.52696704864502
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  32
# batch:  96
i_batch:  0.0
the loss for this batch:  0.3688619
flow loss 0.10526331
occ loss 0.09786952
time for this batch 0.2568368911743164
----------------------------------
i_batch:  64.0
the loss for this batch:  0.41900203
flow loss 0.103023
occ loss 0.1267339
time for this batch 0.29700350761413574
----------------------------------
train loss for this epoch:  0.419207
time for this epoch 35.09530162811279
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  33
# batch:  96
i_batch:  0.0
the loss for this batch:  0.3769046
flow loss 0.090063184
occ loss 0.100339964
time for this batch 0.2255101203918457
----------------------------------
i_batch:  64.0
the loss for this batch:  0.4550749
flow loss 0.1213135
occ loss 0.123904765
time for this batch 0.2682795524597168
----------------------------------
train loss for this epoch:  0.41423
time for this epoch 35.157729387283325
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  34
# batch:  96
i_batch:  0.0
the loss for this batch:  0.35396945
flow loss 0.08546006
occ loss 0.09928854
time for this batch 0.26921677589416504
----------------------------------
i_batch:  64.0
the loss for this batch:  0.46812063
flow loss 0.118434265
occ loss 0.13560781
time for this batch 0.29509854316711426
----------------------------------
train loss for this epoch:  0.409579
time for this epoch 35.83171987533569
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  35
# batch:  96
i_batch:  0.0
the loss for this batch:  0.37681156
flow loss 0.113732874
occ loss 0.09427478
time for this batch 0.2524299621582031
----------------------------------
i_batch:  64.0
the loss for this batch:  0.3939724
flow loss 0.10091384
occ loss 0.12153967
time for this batch 0.30681777000427246
----------------------------------
train loss for this epoch:  0.403603
time for this epoch 36.002930641174316
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  36
# batch:  96
i_batch:  0.0
the loss for this batch:  0.41018596
flow loss 0.10065482
occ loss 0.11418442
time for this batch 0.2620077133178711
----------------------------------
i_batch:  64.0
the loss for this batch:  0.4300273
flow loss 0.11179461
occ loss 0.13118339
time for this batch 0.29457616806030273
----------------------------------
train loss for this epoch:  0.394732
time for this epoch 35.69440460205078
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  37
# batch:  96
i_batch:  0.0
the loss for this batch:  0.35016608
flow loss 0.08908997
occ loss 0.11064067
time for this batch 0.218369722366333
----------------------------------
i_batch:  64.0
the loss for this batch:  0.30897585
flow loss 0.09430329
occ loss 0.08395195
time for this batch 0.2862095832824707
----------------------------------
train loss for this epoch:  0.381798
time for this epoch 35.65648078918457
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  38
# batch:  96
i_batch:  0.0
the loss for this batch:  0.39902508
flow loss 0.10356936
occ loss 0.11990787
time for this batch 0.25845956802368164
----------------------------------
i_batch:  64.0
the loss for this batch:  0.34722477
flow loss 0.092266604
occ loss 0.113696046
time for this batch 0.2992846965789795
----------------------------------
train loss for this epoch:  0.368423
time for this epoch 35.128522872924805
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  39
# batch:  96
i_batch:  0.0
the loss for this batch:  0.41394854
flow loss 0.12348987
occ loss 0.124913044
time for this batch 0.2512226104736328
----------------------------------
i_batch:  64.0
the loss for this batch:  0.31224024
flow loss 0.083869785
occ loss 0.106012546
time for this batch 0.3101785182952881
----------------------------------
train loss for this epoch:  0.35003
time for this epoch 35.84739065170288
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  40
# batch:  96
i_batch:  0.0
the loss for this batch:  0.3051169
flow loss 0.096484736
occ loss 0.10001897
time for this batch 0.25095033645629883
----------------------------------
i_batch:  64.0
the loss for this batch:  0.3504489
flow loss 0.10232294
occ loss 0.1321321
time for this batch 0.30506086349487305
----------------------------------
train loss for this epoch:  0.32895
time for this epoch 34.799466133117676
No_decrease:  7
----------------an epoch starts-------------------
i_epoch:  41
# batch:  96
i_batch:  0.0
the loss for this batch:  0.295658
flow loss 0.083993904
occ loss 0.10921156
time for this batch 0.254105806350708
----------------------------------
i_batch:  64.0
the loss for this batch:  0.22148629
flow loss 0.081246726
occ loss 0.06595018
time for this batch 0.27036237716674805
----------------------------------
train loss for this epoch:  0.306041
time for this epoch 35.50572347640991
No_decrease:  8
----------------an epoch starts-------------------
i_epoch:  42
# batch:  96
i_batch:  0.0
the loss for this batch:  0.3353023
flow loss 0.097058065
occ loss 0.12982446
time for this batch 0.1973581314086914
----------------------------------
i_batch:  64.0
the loss for this batch:  0.29114234
flow loss 0.095731415
occ loss 0.11684513
time for this batch 0.303295373916626
----------------------------------
train loss for this epoch:  0.281347
time for this epoch 34.94415330886841
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  43
# batch:  96
i_batch:  0.0
the loss for this batch:  0.27204332
flow loss 0.08480581
occ loss 0.104991116
time for this batch 0.26788973808288574
----------------------------------
i_batch:  64.0
the loss for this batch:  0.24764699
flow loss 0.07885796
occ loss 0.10824395
time for this batch 0.3014719486236572
----------------------------------
train loss for this epoch:  0.254909
time for this epoch 35.372039794921875
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  44
# batch:  96
i_batch:  0.0
the loss for this batch:  0.24263099
flow loss 0.073979266
occ loss 0.10914522
time for this batch 0.24545502662658691
----------------------------------
i_batch:  64.0
the loss for this batch:  0.22045267
flow loss 0.07377743
occ loss 0.11353393
time for this batch 0.2929513454437256
----------------------------------
train loss for this epoch:  0.232226
time for this epoch 35.94161319732666
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  45
# batch:  96
i_batch:  0.0
the loss for this batch:  0.21767148
flow loss 0.07416888
occ loss 0.110252954
time for this batch 0.24445414543151855
----------------------------------
i_batch:  64.0
the loss for this batch:  0.20003831
flow loss 0.074296035
occ loss 0.10718791
time for this batch 0.2989950180053711
----------------------------------
train loss for this epoch:  0.214334
time for this epoch 36.149083852767944
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  46
# batch:  96
i_batch:  0.0
the loss for this batch:  0.2047111
flow loss 0.07573789
occ loss 0.10929016
time for this batch 0.24228954315185547
----------------------------------
i_batch:  64.0
the loss for this batch:  0.20431921
flow loss 0.07746237
occ loss 0.11293067
time for this batch 0.25021839141845703
----------------------------------
train loss for this epoch:  0.200838
time for this epoch 34.93404197692871
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  47
# batch:  96
i_batch:  0.0
the loss for this batch:  0.21907227
flow loss 0.08033663
occ loss 0.12506995
time for this batch 0.26567983627319336
----------------------------------
i_batch:  64.0
the loss for this batch:  0.22054695
flow loss 0.08543184
occ loss 0.12910184
time for this batch 0.2933344841003418
----------------------------------
train loss for this epoch:  0.193623
time for this epoch 34.762622356414795
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  48
# batch:  96
i_batch:  0.0
the loss for this batch:  0.21236078
flow loss 0.07939019
occ loss 0.12742601
time for this batch 0.2582974433898926
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1787296
flow loss 0.07278452
occ loss 0.101983644
time for this batch 0.28963780403137207
----------------------------------
train loss for this epoch:  0.189209
time for this epoch 36.1998028755188
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  49
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1976913
flow loss 0.07533162
occ loss 0.120719045
time for this batch 0.2443220615386963
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16140793
flow loss 0.062335096
occ loss 0.09640826
time for this batch 0.29781436920166016
----------------------------------
train loss for this epoch:  0.185995
time for this epoch 35.63302159309387
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  50
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1826124
flow loss 0.07059575
occ loss 0.109497964
time for this batch 0.2772204875946045
----------------------------------
i_batch:  64.0
the loss for this batch:  0.22364861
flow loss 0.078151934
occ loss 0.1444472
time for this batch 0.27010178565979004
----------------------------------
train loss for this epoch:  0.184463
time for this epoch 34.803637742996216
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  51
# batch:  96
i_batch:  0.0
the loss for this batch:  0.20148373
flow loss 0.07690339
occ loss 0.12231535
time for this batch 0.2769303321838379
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19337524
flow loss 0.06918977
occ loss 0.12285019
time for this batch 0.28499841690063477
----------------------------------
train loss for this epoch:  0.183613
time for this epoch 35.61232900619507
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  52
# batch:  96
i_batch:  0.0
the loss for this batch:  0.20657057
flow loss 0.07638951
occ loss 0.12892482
time for this batch 0.2516157627105713
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17418733
flow loss 0.07147722
occ loss 0.101738326
time for this batch 0.2968742847442627
----------------------------------
train loss for this epoch:  0.184142
time for this epoch 35.028326749801636
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  53
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16335353
flow loss 0.060786285
occ loss 0.101527
time for this batch 0.261005163192749
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18930201
flow loss 0.07004531
occ loss 0.11829463
time for this batch 0.28554725646972656
----------------------------------
train loss for this epoch:  0.183568
time for this epoch 36.13008451461792
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  54
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18587199
flow loss 0.06421802
occ loss 0.12081852
time for this batch 0.2596447467803955
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1590686
flow loss 0.07073853
occ loss 0.08768538
time for this batch 0.2879030704498291
----------------------------------
train loss for this epoch:  0.181268
time for this epoch 35.76178431510925
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  55
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19883843
flow loss 0.076079406
occ loss 0.121598795
time for this batch 0.2644338607788086
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1849714
flow loss 0.07941136
occ loss 0.104452856
time for this batch 0.303638219833374
----------------------------------
train loss for this epoch:  0.182368
time for this epoch 35.51430892944336
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  56
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14018427
flow loss 0.062171973
occ loss 0.075262174
time for this batch 0.2584266662597656
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17046145
flow loss 0.063576914
occ loss 0.10621622
time for this batch 0.3066260814666748
----------------------------------
train loss for this epoch:  0.182586
time for this epoch 35.705058574676514
No_decrease:  7
----------------an epoch starts-------------------
i_epoch:  57
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1665741
flow loss 0.071154326
occ loss 0.09486615
time for this batch 0.2758338451385498
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16243358
flow loss 0.068643115
occ loss 0.092794806
time for this batch 0.314800500869751
----------------------------------
train loss for this epoch:  0.180245
time for this epoch 36.31664681434631
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  58
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17250068
flow loss 0.06906291
occ loss 0.102777675
time for this batch 0.2737698554992676
----------------------------------
i_batch:  64.0
the loss for this batch:  0.24282578
flow loss 0.0815114
occ loss 0.16064242
time for this batch 0.30222606658935547
----------------------------------
train loss for this epoch:  0.180515
time for this epoch 35.302815198898315
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  59
# batch:  96
i_batch:  0.0
the loss for this batch:  0.20877694
flow loss 0.07987652
occ loss 0.12794197
time for this batch 0.2430739402770996
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17929704
flow loss 0.0748191
occ loss 0.103622526
time for this batch 0.2984952926635742
----------------------------------
train loss for this epoch:  0.18122
time for this epoch 35.12420201301575
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  60
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19129547
flow loss 0.0754445
occ loss 0.11505101
time for this batch 0.26212501525878906
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16212957
flow loss 0.061395224
occ loss 0.09998418
time for this batch 0.2987229824066162
----------------------------------
train loss for this epoch:  0.178272
time for this epoch 36.1417977809906
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  61
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17968763
flow loss 0.07329112
occ loss 0.10561969
time for this batch 0.25141024589538574
----------------------------------
i_batch:  64.0
the loss for this batch:  0.2352563
flow loss 0.08203592
occ loss 0.15223072
time for this batch 0.29625630378723145
----------------------------------
train loss for this epoch:  0.180814
time for this epoch 36.30387997627258
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  62
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16609463
flow loss 0.06716981
occ loss 0.09801696
time for this batch 0.2568655014038086
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17890894
flow loss 0.07816091
occ loss 0.099919416
time for this batch 0.30185556411743164
----------------------------------
train loss for this epoch:  0.179834
time for this epoch 36.11385202407837
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  63
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18904693
flow loss 0.074148074
occ loss 0.11412722
time for this batch 0.22465109825134277
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18088545
flow loss 0.073364
occ loss 0.106486246
time for this batch 0.30543041229248047
----------------------------------
train loss for this epoch:  0.181278
time for this epoch 36.46645951271057
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  64
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16378646
flow loss 0.06444696
occ loss 0.098823905
time for this batch 0.2521672248840332
----------------------------------
i_batch:  64.0
the loss for this batch:  0.2022571
flow loss 0.0819796
occ loss 0.119549334
time for this batch 0.29840970039367676
----------------------------------
train loss for this epoch:  0.179958
time for this epoch 36.30212140083313
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  65
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17393962
flow loss 0.065474406
occ loss 0.10736803
time for this batch 0.2595970630645752
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15900974
flow loss 0.06946608
occ loss 0.0886134
time for this batch 0.29216909408569336
----------------------------------
train loss for this epoch:  0.177619
time for this epoch 36.53178572654724
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  66
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1555474
flow loss 0.06340043
occ loss 0.09121626
time for this batch 0.2494795322418213
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1547137
flow loss 0.06493128
occ loss 0.08897761
time for this batch 0.2768528461456299
----------------------------------
train loss for this epoch:  0.177394
time for this epoch 34.96186017990112
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  67
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16221932
flow loss 0.061696123
occ loss 0.099805705
time for this batch 0.24313807487487793
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17618465
flow loss 0.0682056
occ loss 0.107261226
time for this batch 0.2983405590057373
----------------------------------
train loss for this epoch:  0.176097
time for this epoch 36.046433448791504
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  68
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15187088
flow loss 0.05862864
occ loss 0.09247688
time for this batch 0.25472474098205566
----------------------------------
i_batch:  64.0
the loss for this batch:  0.20847256
flow loss 0.07701848
occ loss 0.13086286
time for this batch 0.30264949798583984
----------------------------------
train loss for this epoch:  0.178273
time for this epoch 36.27314329147339
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  69
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17417818
flow loss 0.07278713
occ loss 0.100539796
time for this batch 0.2670280933380127
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18541418
flow loss 0.068778165
occ loss 0.11589819
time for this batch 0.3021848201751709
----------------------------------
train loss for this epoch:  0.178246
time for this epoch 35.856016397476196
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  70
# batch:  96
i_batch:  0.0
the loss for this batch:  0.21581231
flow loss 0.07632032
occ loss 0.13860479
time for this batch 0.2787656784057617
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18557203
flow loss 0.07006866
occ loss 0.11442894
time for this batch 0.2810988426208496
----------------------------------
train loss for this epoch:  0.176841
time for this epoch 36.010557889938354
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  71
# batch:  96
i_batch:  0.0
the loss for this batch:  0.23150471
flow loss 0.0750489
occ loss 0.15562907
time for this batch 0.2546401023864746
----------------------------------
i_batch:  64.0
the loss for this batch:  0.21260475
flow loss 0.08089239
occ loss 0.13069199
time for this batch 0.3048975467681885
----------------------------------
train loss for this epoch:  0.176553
time for this epoch 36.047274112701416
No_decrease:  7
----------------an epoch starts-------------------
i_epoch:  72
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14073075
flow loss 0.06269498
occ loss 0.077376574
time for this batch 0.25598883628845215
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19906275
flow loss 0.07520309
occ loss 0.12275161
time for this batch 0.28632020950317383
----------------------------------
train loss for this epoch:  0.178198
time for this epoch 36.027724504470825
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  73
# batch:  96
i_batch:  0.0
the loss for this batch:  0.12857373
flow loss 0.053558815
occ loss 0.07375705
time for this batch 0.25538039207458496
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14640312
flow loss 0.062266264
occ loss 0.08337094
time for this batch 0.29807186126708984
----------------------------------
train loss for this epoch:  0.176748
time for this epoch 35.78542947769165
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  74
# batch:  96
i_batch:  0.0
the loss for this batch:  0.20540783
flow loss 0.07356361
occ loss 0.13089906
time for this batch 0.2588331699371338
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18364294
flow loss 0.06570559
occ loss 0.117073804
time for this batch 0.2921876907348633
----------------------------------
train loss for this epoch:  0.175068
time for this epoch 35.899118185043335
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  75
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14309426
flow loss 0.062207323
occ loss 0.07974935
time for this batch 0.2660961151123047
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17353748
flow loss 0.06697091
occ loss 0.10580147
time for this batch 0.28576016426086426
----------------------------------
train loss for this epoch:  0.175597
time for this epoch 35.56472587585449
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  76
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14805058
flow loss 0.05894515
occ loss 0.08843532
time for this batch 0.2700047492980957
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19052269
flow loss 0.0713113
occ loss 0.11819305
time for this batch 0.2916395664215088
----------------------------------
train loss for this epoch:  0.174831
time for this epoch 35.850205421447754
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  77
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19912404
flow loss 0.07374568
occ loss 0.124759026
time for this batch 0.2669544219970703
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18986535
flow loss 0.07092313
occ loss 0.118022814
time for this batch 0.2703282833099365
----------------------------------
train loss for this epoch:  0.175375
time for this epoch 35.58188247680664
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  78
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16870596
flow loss 0.06469554
occ loss 0.10319823
time for this batch 0.23950910568237305
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14497223
flow loss 0.05811251
occ loss 0.08560113
time for this batch 0.26145434379577637
----------------------------------
train loss for this epoch:  0.175711
time for this epoch 35.62810683250427
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  79
# batch:  96
i_batch:  0.0
the loss for this batch:  0.22210418
flow loss 0.07783774
occ loss 0.14343894
time for this batch 0.23857855796813965
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19293754
flow loss 0.069531105
occ loss 0.12278404
time for this batch 0.3013112545013428
----------------------------------
train loss for this epoch:  0.174162
time for this epoch 36.09221267700195
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  80
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19990097
flow loss 0.07042428
occ loss 0.12865004
time for this batch 0.24941492080688477
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19666727
flow loss 0.075448655
occ loss 0.12039861
time for this batch 0.2980198860168457
----------------------------------
train loss for this epoch:  0.174453
time for this epoch 35.82158279418945
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  81
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17310116
flow loss 0.06615764
occ loss 0.1060185
time for this batch 0.2541351318359375
----------------------------------
i_batch:  64.0
the loss for this batch:  0.20560594
flow loss 0.07974369
occ loss 0.12422366
time for this batch 0.30580711364746094
----------------------------------
train loss for this epoch:  0.176544
time for this epoch 35.67205810546875
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  82
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16451256
flow loss 0.06395296
occ loss 0.09997208
time for this batch 0.2365109920501709
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1755137
flow loss 0.066751145
occ loss 0.107194304
time for this batch 0.27672529220581055
----------------------------------
train loss for this epoch:  0.174006
time for this epoch 35.319366693496704
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  83
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18455452
flow loss 0.07175092
occ loss 0.111757845
time for this batch 0.2568793296813965
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1818819
flow loss 0.06571149
occ loss 0.11519695
time for this batch 0.2887842655181885
----------------------------------
train loss for this epoch:  0.173423
time for this epoch 36.22359919548035
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  84
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16761215
flow loss 0.06283234
occ loss 0.104054235
time for this batch 0.2525951862335205
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15858328
flow loss 0.064277925
occ loss 0.09339289
time for this batch 0.2982490062713623
----------------------------------
train loss for this epoch:  0.17653
time for this epoch 36.12782144546509
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  85
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14822574
flow loss 0.066589184
occ loss 0.08093734
time for this batch 0.2663259506225586
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17947254
flow loss 0.07253623
occ loss 0.10601501
time for this batch 0.3018186092376709
----------------------------------
train loss for this epoch:  0.173782
time for this epoch 35.41782283782959
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  86
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16392288
flow loss 0.062346783
occ loss 0.10083972
time for this batch 0.2701094150543213
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14363073
flow loss 0.05962032
occ loss 0.083401255
time for this batch 0.2984335422515869
----------------------------------
train loss for this epoch:  0.174515
time for this epoch 35.94626045227051
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  87
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16845307
flow loss 0.06861934
occ loss 0.09869199
time for this batch 0.26525330543518066
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17226888
flow loss 0.0699509
occ loss 0.10126868
time for this batch 0.2998464107513428
----------------------------------
train loss for this epoch:  0.172742
time for this epoch 35.28374361991882
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  88
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17036267
flow loss 0.06874661
occ loss 0.10051472
time for this batch 0.27605175971984863
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1921688
flow loss 0.07044844
occ loss 0.12089019
time for this batch 0.28609585762023926
----------------------------------
train loss for this epoch:  0.172419
time for this epoch 35.75591683387756
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  89
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1437006
flow loss 0.066267565
occ loss 0.07631171
time for this batch 0.2639925479888916
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18563831
flow loss 0.069431394
occ loss 0.11537125
time for this batch 0.27533793449401855
----------------------------------
train loss for this epoch:  0.171766
time for this epoch 34.9286367893219
No_decrease:  7
----------------an epoch starts-------------------
i_epoch:  90
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1899916
flow loss 0.066792786
occ loss 0.12183957
time for this batch 0.26248764991760254
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17338726
flow loss 0.06592671
occ loss 0.10666962
time for this batch 0.2870357036590576
----------------------------------
train loss for this epoch:  0.172418
time for this epoch 35.84917235374451
No_decrease:  8
----------------an epoch starts-------------------
i_epoch:  91
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1669523
flow loss 0.06796462
occ loss 0.09804562
time for this batch 0.2639782428741455
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17971894
flow loss 0.06703968
occ loss 0.111628935
time for this batch 0.3011813163757324
----------------------------------
train loss for this epoch:  0.172649
time for this epoch 36.00700616836548
No_decrease:  9
----------------an epoch starts-------------------
i_epoch:  92
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13031538
flow loss 0.055755705
occ loss 0.07358056
time for this batch 0.2678096294403076
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18614526
flow loss 0.06694396
occ loss 0.11861219
time for this batch 0.30106234550476074
----------------------------------
train loss for this epoch:  0.170485
time for this epoch 35.92256283760071
No_decrease:  10
----------------an epoch starts-------------------
i_epoch:  93
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17399646
flow loss 0.0704782
occ loss 0.10285781
time for this batch 0.26882338523864746
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14654066
flow loss 0.06561657
occ loss 0.07990454
time for this batch 0.26110100746154785
----------------------------------
train loss for this epoch:  0.171181
time for this epoch 35.136146545410156
No_decrease:  11
----------------an epoch starts-------------------
i_epoch:  94
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17925906
flow loss 0.06961416
occ loss 0.10907468
time for this batch 0.26853108406066895
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16175982
flow loss 0.06334969
occ loss 0.09751119
time for this batch 0.2962038516998291
----------------------------------
train loss for this epoch:  0.172813
time for this epoch 35.84126567840576
No_decrease:  12
----------------an epoch starts-------------------
i_epoch:  95
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13919745
flow loss 0.06252044
occ loss 0.07566107
time for this batch 0.265622615814209
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17003743
flow loss 0.06402331
occ loss 0.10473329
time for this batch 0.2732689380645752
----------------------------------
train loss for this epoch:  0.170329
time for this epoch 34.65124297142029
No_decrease:  13
----------------an epoch starts-------------------
i_epoch:  96
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1824263
flow loss 0.067468
occ loss 0.11392409
time for this batch 0.24829959869384766
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15472004
flow loss 0.06327815
occ loss 0.0906212
time for this batch 0.25166773796081543
----------------------------------
train loss for this epoch:  0.172215
time for this epoch 33.35471439361572
No_decrease:  14
----------------an epoch starts-------------------
i_epoch:  97
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15053113
flow loss 0.06430782
occ loss 0.08565785
time for this batch 0.26958131790161133
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13777101
flow loss 0.057096854
occ loss 0.079850085
time for this batch 0.2913544178009033
----------------------------------
train loss for this epoch:  0.171034
time for this epoch 35.63239336013794
No_decrease:  15
----------------an epoch starts-------------------
i_epoch:  98
# batch:  96
i_batch:  0.0
the loss for this batch:  0.22729017
flow loss 0.07047774
occ loss 0.15584037
time for this batch 0.25759220123291016
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1784747
flow loss 0.070728585
occ loss 0.10641592
time for this batch 0.28887271881103516
----------------------------------
train loss for this epoch:  0.17117
time for this epoch 35.95522618293762
No_decrease:  16
----------------an epoch starts-------------------
i_epoch:  99
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19365247
flow loss 0.0773244
occ loss 0.11559213
time for this batch 0.2680821418762207
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19511029
flow loss 0.072318144
occ loss 0.121697575
time for this batch 0.3059711456298828
----------------------------------
train loss for this epoch:  0.17114
time for this epoch 36.03052806854248
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  100
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18382917
flow loss 0.07142087
occ loss 0.111550584
time for this batch 0.2662816047668457
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14682268
flow loss 0.05985432
occ loss 0.08621209
time for this batch 0.29518604278564453
----------------------------------
train loss for this epoch:  0.171666
time for this epoch 35.27071404457092
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  101
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13862193
flow loss 0.05571521
occ loss 0.08208578
time for this batch 0.27253127098083496
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19145031
flow loss 0.06771027
occ loss 0.12308605
time for this batch 0.2843635082244873
----------------------------------
train loss for this epoch:  0.169693
time for this epoch 36.302613496780396
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  102
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19748104
flow loss 0.071220726
occ loss 0.12530187
time for this batch 0.23814082145690918
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14927016
flow loss 0.059995897
occ loss 0.08863657
time for this batch 0.2110915184020996
----------------------------------
train loss for this epoch:  0.168606
time for this epoch 35.499664068222046
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  103
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14776675
flow loss 0.05654737
occ loss 0.090597115
time for this batch 0.25837254524230957
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15584572
flow loss 0.0700638
occ loss 0.08521566
time for this batch 0.28821682929992676
----------------------------------
train loss for this epoch:  0.170635
time for this epoch 35.85975956916809
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  104
# batch:  96
i_batch:  0.0
the loss for this batch:  0.20048954
flow loss 0.07721583
occ loss 0.12150759
time for this batch 0.24973273277282715
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16130173
flow loss 0.06123617
occ loss 0.099188745
time for this batch 0.2896862030029297
----------------------------------
train loss for this epoch:  0.168797
time for this epoch 35.19462060928345
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  105
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16971557
flow loss 0.065887526
occ loss 0.102793045
time for this batch 0.2492680549621582
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18537907
flow loss 0.066772915
occ loss 0.11778651
time for this batch 0.29853081703186035
----------------------------------
train loss for this epoch:  0.172405
time for this epoch 35.25805687904358
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  106
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18608963
flow loss 0.065567374
occ loss 0.119753346
time for this batch 0.25777268409729004
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14583428
flow loss 0.060112637
occ loss 0.08491774
time for this batch 0.290330171585083
----------------------------------
train loss for this epoch:  0.169008
time for this epoch 35.82243514060974
No_decrease:  7
----------------an epoch starts-------------------
i_epoch:  107
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15132196
flow loss 0.059302967
occ loss 0.09086312
time for this batch 0.2657811641693115
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19752368
flow loss 0.06871441
occ loss 0.1280359
time for this batch 0.30043840408325195
----------------------------------
train loss for this epoch:  0.168041
time for this epoch 36.217986822128296
No_decrease:  8
----------------an epoch starts-------------------
i_epoch:  108
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14523916
flow loss 0.05943706
occ loss 0.085010126
time for this batch 0.2522103786468506
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14921159
flow loss 0.06480461
occ loss 0.0832153
time for this batch 0.2931358814239502
----------------------------------
train loss for this epoch:  0.168551
time for this epoch 36.54414439201355
No_decrease:  9
----------------an epoch starts-------------------
i_epoch:  109
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18801223
flow loss 0.0755666
occ loss 0.11156944
time for this batch 0.2644948959350586
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17133515
flow loss 0.06323689
occ loss 0.10718493
time for this batch 0.29678964614868164
----------------------------------
train loss for this epoch:  0.169542
time for this epoch 36.99984073638916
No_decrease:  10
----------------an epoch starts-------------------
i_epoch:  110
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18474686
flow loss 0.06602886
occ loss 0.11794116
time for this batch 0.24725794792175293
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14282207
flow loss 0.060438827
occ loss 0.08176793
time for this batch 0.29830169677734375
----------------------------------
train loss for this epoch:  0.168295
time for this epoch 35.71513485908508
No_decrease:  11
----------------an epoch starts-------------------
i_epoch:  111
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17751063
flow loss 0.07513848
occ loss 0.101702005
time for this batch 0.26201438903808594
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19898699
flow loss 0.070259884
occ loss 0.12775458
time for this batch 0.3097550868988037
----------------------------------
train loss for this epoch:  0.168511
time for this epoch 35.73501110076904
No_decrease:  12
----------------an epoch starts-------------------
i_epoch:  112
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18094252
flow loss 0.064714424
occ loss 0.11537253
time for this batch 0.2692084312438965
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15326537
flow loss 0.06480347
occ loss 0.0878365
time for this batch 0.2810053825378418
----------------------------------
train loss for this epoch:  0.167722
time for this epoch 36.388078927993774
No_decrease:  13
----------------an epoch starts-------------------
i_epoch:  113
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18841113
flow loss 0.069949426
occ loss 0.11775675
time for this batch 0.2638967037200928
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1506983
flow loss 0.06491655
occ loss 0.08487445
time for this batch 0.3000068664550781
----------------------------------
train loss for this epoch:  0.168696
time for this epoch 36.22943043708801
No_decrease:  14
----------------an epoch starts-------------------
i_epoch:  114
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1513195
flow loss 0.061665587
occ loss 0.08919769
time for this batch 0.2660079002380371
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14817843
flow loss 0.05838098
occ loss 0.08905777
time for this batch 0.3106727600097656
----------------------------------
train loss for this epoch:  0.167337
time for this epoch 36.21411466598511
No_decrease:  15
----------------an epoch starts-------------------
i_epoch:  115
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17068912
flow loss 0.065765105
occ loss 0.104191154
time for this batch 0.2539398670196533
----------------------------------
i_batch:  64.0
the loss for this batch:  0.179992
flow loss 0.06274154
occ loss 0.116555765
time for this batch 0.2882530689239502
----------------------------------
train loss for this epoch:  0.167658
time for this epoch 36.06180429458618
No_decrease:  16
----------------an epoch starts-------------------
i_epoch:  116
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16078553
flow loss 0.062046073
occ loss 0.09775417
time for this batch 0.25798892974853516
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13351859
flow loss 0.058307458
occ loss 0.07422486
time for this batch 0.23889732360839844
----------------------------------
train loss for this epoch:  0.166971
time for this epoch 35.747259855270386
No_decrease:  17
----------------an epoch starts-------------------
i_epoch:  117
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19687915
flow loss 0.06595999
occ loss 0.1299097
time for this batch 0.2570075988769531
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19140714
flow loss 0.06877185
occ loss 0.12182631
time for this batch 0.2782907485961914
----------------------------------
train loss for this epoch:  0.166952
time for this epoch 35.54017734527588
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  118
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15992452
flow loss 0.06467429
occ loss 0.09442791
time for this batch 0.2734537124633789
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17479111
flow loss 0.06155282
occ loss 0.11190727
time for this batch 0.3027524948120117
----------------------------------
train loss for this epoch:  0.167984
time for this epoch 35.28569555282593
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  119
# batch:  96
i_batch:  0.0
the loss for this batch:  0.21105213
flow loss 0.07216683
occ loss 0.13797064
time for this batch 0.25921201705932617
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15090664
flow loss 0.056996893
occ loss 0.09259641
time for this batch 0.29767537117004395
----------------------------------
train loss for this epoch:  0.166573
time for this epoch 36.52397584915161
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  120
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18526545
flow loss 0.06808173
occ loss 0.11605248
time for this batch 0.2691330909729004
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14845903
flow loss 0.05554735
occ loss 0.09249969
time for this batch 0.2902567386627197
----------------------------------
train loss for this epoch:  0.165953
time for this epoch 36.09651708602905
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  121
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15452021
flow loss 0.05586844
occ loss 0.097964615
time for this batch 0.26357483863830566
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18051723
flow loss 0.064815454
occ loss 0.11482169
time for this batch 0.3009369373321533
----------------------------------
train loss for this epoch:  0.165767
time for this epoch 36.065746545791626
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  122
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18440987
flow loss 0.06527475
occ loss 0.11754483
time for this batch 0.2586243152618408
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19673747
flow loss 0.07269813
occ loss 0.12275551
time for this batch 0.30745935440063477
----------------------------------
train loss for this epoch:  0.166726
time for this epoch 35.79621505737305
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  123
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1756933
flow loss 0.06474559
occ loss 0.110019684
time for this batch 0.26284050941467285
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16327827
flow loss 0.065372735
occ loss 0.09713858
time for this batch 0.26116037368774414
----------------------------------
train loss for this epoch:  0.16686
time for this epoch 36.291017293930054
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  124
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13908443
flow loss 0.056497842
occ loss 0.08153422
time for this batch 0.2691831588745117
----------------------------------
i_batch:  64.0
the loss for this batch:  0.20510605
flow loss 0.076941825
occ loss 0.12755738
time for this batch 0.3001992702484131
----------------------------------
train loss for this epoch:  0.166072
time for this epoch 35.80522918701172
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  125
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19285603
flow loss 0.062092416
occ loss 0.13007852
time for this batch 0.26743173599243164
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17719546
flow loss 0.06736508
occ loss 0.10915974
time for this batch 0.2804434299468994
----------------------------------
train loss for this epoch:  0.168625
time for this epoch 35.78720760345459
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  126
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13736126
flow loss 0.06473019
occ loss 0.07148071
time for this batch 0.2519512176513672
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15064935
flow loss 0.05813848
occ loss 0.09185129
time for this batch 0.29706454277038574
----------------------------------
train loss for this epoch:  0.166519
time for this epoch 35.94016718864441
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  127
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17597789
flow loss 0.06695326
occ loss 0.10811217
time for this batch 0.24572300910949707
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16476296
flow loss 0.059370767
occ loss 0.104167685
time for this batch 0.3047666549682617
----------------------------------
train loss for this epoch:  0.167058
time for this epoch 36.441943645477295
No_decrease:  7
----------------an epoch starts-------------------
i_epoch:  128
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15974542
flow loss 0.061560277
occ loss 0.09747472
time for this batch 0.2700495719909668
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17378312
flow loss 0.061568044
occ loss 0.11136644
time for this batch 0.3208801746368408
----------------------------------
train loss for this epoch:  0.165275
time for this epoch 35.56499886512756
No_decrease:  8
----------------an epoch starts-------------------
i_epoch:  129
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14911258
flow loss 0.060733285
occ loss 0.08722798
time for this batch 0.29329442977905273
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16176897
flow loss 0.06487979
occ loss 0.09624905
time for this batch 0.299100399017334
----------------------------------
train loss for this epoch:  0.164976
time for this epoch 36.05239772796631
No_decrease:  9
----------------an epoch starts-------------------
i_epoch:  130
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16111392
flow loss 0.05791893
occ loss 0.1026149
time for this batch 0.2617664337158203
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18926121
flow loss 0.066344455
occ loss 0.12215179
time for this batch 0.2985191345214844
----------------------------------
train loss for this epoch:  0.164508
time for this epoch 34.91592335700989
No_decrease:  10
----------------an epoch starts-------------------
i_epoch:  131
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17399368
flow loss 0.06280025
occ loss 0.110477746
time for this batch 0.2521655559539795
----------------------------------
i_batch:  64.0
the loss for this batch:  0.12310291
flow loss 0.053632878
occ loss 0.06887927
time for this batch 0.3116931915283203
----------------------------------
train loss for this epoch:  0.165528
time for this epoch 36.113171339035034
No_decrease:  11
----------------an epoch starts-------------------
i_epoch:  132
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19262879
flow loss 0.07276632
occ loss 0.11890479
time for this batch 0.2547125816345215
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19775216
flow loss 0.070159175
occ loss 0.12678385
time for this batch 0.27264952659606934
----------------------------------
train loss for this epoch:  0.165681
time for this epoch 35.66111731529236
No_decrease:  12
----------------an epoch starts-------------------
i_epoch:  133
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14820036
flow loss 0.057528228
occ loss 0.089986004
time for this batch 0.22443127632141113
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14508349
flow loss 0.063489065
occ loss 0.08078793
time for this batch 0.2763831615447998
----------------------------------
train loss for this epoch:  0.16799
time for this epoch 35.596271991729736
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  134
# batch:  96
i_batch:  0.0
the loss for this batch:  0.20301802
flow loss 0.07096529
occ loss 0.13122286
time for this batch 0.26012325286865234
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16000837
flow loss 0.064371645
occ loss 0.094972245
time for this batch 0.308544397354126
----------------------------------
train loss for this epoch:  0.163128
time for this epoch 35.961509227752686
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  135
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14701864
flow loss 0.062383514
occ loss 0.083751686
time for this batch 0.24263739585876465
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17839578
flow loss 0.061300468
occ loss 0.11613542
time for this batch 0.29031896591186523
----------------------------------
train loss for this epoch:  0.162938
time for this epoch 36.55561661720276
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  136
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15910903
flow loss 0.065040104
occ loss 0.09348571
time for this batch 0.2041635513305664
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19327112
flow loss 0.067380995
occ loss 0.1251332
time for this batch 0.2885456085205078
----------------------------------
train loss for this epoch:  0.164777
time for this epoch 36.116127252578735
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  137
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18838324
flow loss 0.06526476
occ loss 0.12208693
time for this batch 0.25025320053100586
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16050997
flow loss 0.05995745
occ loss 0.09965484
time for this batch 0.2805347442626953
----------------------------------
train loss for this epoch:  0.166455
time for this epoch 36.00494861602783
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  138
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13365744
flow loss 0.057081107
occ loss 0.0759894
time for this batch 0.26805925369262695
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17442197
flow loss 0.065109305
occ loss 0.10826822
time for this batch 0.29799890518188477
----------------------------------
train loss for this epoch:  0.162944
time for this epoch 36.234371185302734
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  139
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14453746
flow loss 0.05975117
occ loss 0.08396948
time for this batch 0.2545480728149414
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18015681
flow loss 0.06515313
occ loss 0.11424621
time for this batch 0.2914907932281494
----------------------------------
train loss for this epoch:  0.164454
time for this epoch 35.85138177871704
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  140
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14195777
flow loss 0.059396025
occ loss 0.0814118
time for this batch 0.2241818904876709
----------------------------------
i_batch:  64.0
the loss for this batch:  0.12662634
flow loss 0.05306501
occ loss 0.07276199
time for this batch 0.295123815536499
----------------------------------
train loss for this epoch:  0.164236
time for this epoch 36.283220052719116
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  141
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16138643
flow loss 0.061501015
occ loss 0.09922265
time for this batch 0.26515865325927734
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16881907
flow loss 0.06487782
occ loss 0.10320298
time for this batch 0.2851681709289551
----------------------------------
train loss for this epoch:  0.162434
time for this epoch 36.55703520774841
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  142
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1547023
flow loss 0.058712777
occ loss 0.095323026
time for this batch 0.25592827796936035
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18592538
flow loss 0.06794815
occ loss 0.116724014
time for this batch 0.2920231819152832
----------------------------------
train loss for this epoch:  0.164301
time for this epoch 36.09674620628357
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  143
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1450916
flow loss 0.059892073
occ loss 0.08434885
time for this batch 0.25765037536621094
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17056878
flow loss 0.06432403
occ loss 0.105539754
time for this batch 0.29903268814086914
----------------------------------
train loss for this epoch:  0.162526
time for this epoch 36.021533489227295
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  144
# batch:  96
i_batch:  0.0
the loss for this batch:  0.21400306
flow loss 0.068346344
occ loss 0.14511472
time for this batch 0.2710764408111572
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1588481
flow loss 0.06310648
occ loss 0.09495549
time for this batch 0.30779075622558594
----------------------------------
train loss for this epoch:  0.162888
time for this epoch 36.1134819984436
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  145
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18575923
flow loss 0.06331387
occ loss 0.121992365
time for this batch 0.25138044357299805
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16570902
flow loss 0.06108936
occ loss 0.10402548
time for this batch 0.2991189956665039
----------------------------------
train loss for this epoch:  0.164397
time for this epoch 35.776241064071655
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  146
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13487156
flow loss 0.055854507
occ loss 0.077206254
time for this batch 0.21877503395080566
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14876162
flow loss 0.05866957
occ loss 0.08909258
time for this batch 0.300675630569458
----------------------------------
train loss for this epoch:  0.162583
time for this epoch 36.34780502319336
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  147
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1532793
flow loss 0.057343688
occ loss 0.09507453
time for this batch 0.20897412300109863
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1945971
flow loss 0.06833434
occ loss 0.12564114
time for this batch 0.30044007301330566
----------------------------------
train loss for this epoch:  0.162201
time for this epoch 36.418190002441406
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  148
# batch:  96
i_batch:  0.0
the loss for this batch:  0.19601624
flow loss 0.06716528
occ loss 0.12820274
time for this batch 0.25798797607421875
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16467153
flow loss 0.06356262
occ loss 0.10040433
time for this batch 0.29102063179016113
----------------------------------
train loss for this epoch:  0.163255
time for this epoch 36.58564519882202
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  149
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15211977
flow loss 0.056989487
occ loss 0.09447543
time for this batch 0.2594292163848877
----------------------------------
i_batch:  64.0
the loss for this batch:  0.12770762
flow loss 0.054798875
occ loss 0.072361134
time for this batch 0.29372477531433105
----------------------------------
train loss for this epoch:  0.16273
time for this epoch 36.15956139564514
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  150
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13788454
flow loss 0.06379525
occ loss 0.07346133
time for this batch 0.26778626441955566
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16940837
flow loss 0.05783142
occ loss 0.11073641
time for this batch 0.2682514190673828
----------------------------------
train loss for this epoch:  0.15662
time for this epoch 36.560269594192505
No_decrease:  0
----------------an epoch starts-------------------
i_epoch:  151
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13520505
flow loss 0.05599338
occ loss 0.078519255
time for this batch 0.2735929489135742
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16274416
flow loss 0.061264355
occ loss 0.10076438
time for this batch 0.27887630462646484
----------------------------------
train loss for this epoch:  0.153491
time for this epoch 36.28751468658447
No_decrease:  1
----------------an epoch starts-------------------
i_epoch:  152
# batch:  96
i_batch:  0.0
the loss for this batch:  0.12726319
flow loss 0.05173375
occ loss 0.07484029
time for this batch 0.26604270935058594
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17634547
flow loss 0.06100433
occ loss 0.114343
time for this batch 0.296708345413208
----------------------------------
train loss for this epoch:  0.153264
time for this epoch 36.4266197681427
No_decrease:  2
----------------an epoch starts-------------------
i_epoch:  153
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15570161
flow loss 0.056850456
occ loss 0.09804322
time for this batch 0.22124457359313965
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16447847
flow loss 0.0605166
occ loss 0.10267027
time for this batch 0.29894256591796875
----------------------------------
train loss for this epoch:  0.152769
time for this epoch 35.909162759780884
No_decrease:  3
----------------an epoch starts-------------------
i_epoch:  154
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14046271
flow loss 0.057771005
occ loss 0.081953324
time for this batch 0.27202844619750977
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16099103
flow loss 0.055576876
occ loss 0.10485298
time for this batch 0.2832646369934082
----------------------------------
train loss for this epoch:  0.152905
time for this epoch 35.63705587387085
No_decrease:  4
----------------an epoch starts-------------------
i_epoch:  155
# batch:  96
i_batch:  0.0
the loss for this batch:  0.10214542
flow loss 0.04948039
occ loss 0.05209579
time for this batch 0.2649726867675781
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1633307
flow loss 0.059093297
occ loss 0.10349911
time for this batch 0.29421544075012207
----------------------------------
train loss for this epoch:  0.152138
time for this epoch 35.542768478393555
No_decrease:  5
----------------an epoch starts-------------------
i_epoch:  156
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16001342
flow loss 0.058796212
occ loss 0.100387745
time for this batch 0.266587495803833
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1663393
flow loss 0.056855183
occ loss 0.10882389
time for this batch 0.3000004291534424
----------------------------------
train loss for this epoch:  0.151996
time for this epoch 35.83282160758972
No_decrease:  6
----------------an epoch starts-------------------
i_epoch:  157
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15057373
flow loss 0.054906603
occ loss 0.094982624
time for this batch 0.2648301124572754
----------------------------------
i_batch:  64.0
the loss for this batch:  0.20232894
flow loss 0.06693005
occ loss 0.1347382
time for this batch 0.2764742374420166
----------------------------------
train loss for this epoch:  0.151977
time for this epoch 36.30727672576904
No_decrease:  7
----------------an epoch starts-------------------
i_epoch:  158
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15180667
flow loss 0.056319684
occ loss 0.09482578
time for this batch 0.2681007385253906
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13946186
flow loss 0.056474905
occ loss 0.08218024
time for this batch 0.30027008056640625
----------------------------------
train loss for this epoch:  0.151813
time for this epoch 35.29623460769653
No_decrease:  8
----------------an epoch starts-------------------
i_epoch:  159
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16296007
flow loss 0.057837028
occ loss 0.10442531
time for this batch 0.2708306312561035
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13768955
flow loss 0.05264592
occ loss 0.08440288
time for this batch 0.3014228343963623
----------------------------------
train loss for this epoch:  0.151632
time for this epoch 35.6601722240448
No_decrease:  9
----------------an epoch starts-------------------
i_epoch:  160
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1406896
flow loss 0.05565457
occ loss 0.083998576
time for this batch 0.2620675563812256
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17966387
flow loss 0.06071053
occ loss 0.11832286
time for this batch 0.3087944984436035
----------------------------------
train loss for this epoch:  0.151575
time for this epoch 35.05521368980408
No_decrease:  10
----------------an epoch starts-------------------
i_epoch:  161
# batch:  96
i_batch:  0.0
the loss for this batch:  0.12369535
flow loss 0.05111572
occ loss 0.071599156
time for this batch 0.2744259834289551
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14398728
flow loss 0.0539183
occ loss 0.08959059
time for this batch 0.2773172855377197
----------------------------------
train loss for this epoch:  0.152055
time for this epoch 36.03665375709534
No_decrease:  11
----------------an epoch starts-------------------
i_epoch:  162
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13523693
flow loss 0.052544214
occ loss 0.082028404
time for this batch 0.27314162254333496
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14475445
flow loss 0.055983353
occ loss 0.08804973
time for this batch 0.30843091011047363
----------------------------------
train loss for this epoch:  0.151681
time for this epoch 36.689247369766235
No_decrease:  12
----------------an epoch starts-------------------
i_epoch:  163
# batch:  96
i_batch:  0.0
the loss for this batch:  0.12454147
flow loss 0.051096443
occ loss 0.0728236
time for this batch 0.2681999206542969
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15612128
flow loss 0.05755154
occ loss 0.09796028
time for this batch 0.2992880344390869
----------------------------------
train loss for this epoch:  0.151599
time for this epoch 36.065070390701294
No_decrease:  13
----------------an epoch starts-------------------
i_epoch:  164
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16282469
flow loss 0.05785704
occ loss 0.10447369
time for this batch 0.26404762268066406
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16498072
flow loss 0.060133424
occ loss 0.10386911
time for this batch 0.29967761039733887
----------------------------------
train loss for this epoch:  0.15153
time for this epoch 35.42187261581421
No_decrease:  14
----------------an epoch starts-------------------
i_epoch:  165
# batch:  96
i_batch:  0.0
the loss for this batch:  0.12908065
flow loss 0.052725412
occ loss 0.075535566
time for this batch 0.262225866317749
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15927891
flow loss 0.058527928
occ loss 0.10011309
time for this batch 0.2968783378601074
----------------------------------
train loss for this epoch:  0.151535
time for this epoch 36.28017783164978
No_decrease:  15
----------------an epoch starts-------------------
i_epoch:  166
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16636567
flow loss 0.056342505
occ loss 0.1093123
time for this batch 0.27455663681030273
----------------------------------
i_batch:  64.0
the loss for this batch:  0.19557306
flow loss 0.06426797
occ loss 0.13063443
time for this batch 0.30594515800476074
----------------------------------
train loss for this epoch:  0.151632
time for this epoch 34.537835121154785
No_decrease:  16
----------------an epoch starts-------------------
i_epoch:  167
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16845353
flow loss 0.055725556
occ loss 0.11194516
time for this batch 0.25351381301879883
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15966389
flow loss 0.05474414
occ loss 0.10411117
time for this batch 0.2638378143310547
----------------------------------
train loss for this epoch:  0.151117
time for this epoch 35.91218066215515
No_decrease:  17
----------------an epoch starts-------------------
i_epoch:  168
# batch:  96
i_batch:  0.0
the loss for this batch:  0.10159688
flow loss 0.045155793
occ loss 0.055603415
time for this batch 0.21937203407287598
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13584958
flow loss 0.052537255
occ loss 0.082549885
time for this batch 0.30303049087524414
----------------------------------
train loss for this epoch:  0.151119
time for this epoch 35.270124197006226
No_decrease:  18
----------------an epoch starts-------------------
i_epoch:  169
# batch:  96
i_batch:  0.0
the loss for this batch:  0.1310682
flow loss 0.05327877
occ loss 0.07710029
time for this batch 0.23630809783935547
----------------------------------
i_batch:  64.0
the loss for this batch:  0.16685642
flow loss 0.06219238
occ loss 0.10412561
time for this batch 0.2972850799560547
----------------------------------
train loss for this epoch:  0.15121
time for this epoch 36.329983949661255
No_decrease:  19
----------------an epoch starts-------------------
i_epoch:  170
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14618158
flow loss 0.054661352
occ loss 0.09064104
time for this batch 0.2447960376739502
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13900916
flow loss 0.05394424
occ loss 0.08416267
time for this batch 0.25426769256591797
----------------------------------
train loss for this epoch:  0.151134
time for this epoch 33.09652829170227
No_decrease:  20
----------------an epoch starts-------------------
i_epoch:  171
# batch:  96
i_batch:  0.0
the loss for this batch:  0.14672814
flow loss 0.05546782
occ loss 0.09065321
time for this batch 0.28752708435058594
----------------------------------
i_batch:  64.0
the loss for this batch:  0.14392416
flow loss 0.053741593
occ loss 0.08963703
time for this batch 0.29972314834594727
----------------------------------
train loss for this epoch:  0.15115
time for this epoch 35.96700930595398
No_decrease:  21
----------------an epoch starts-------------------
i_epoch:  172
# batch:  96
i_batch:  0.0
the loss for this batch:  0.12118472
flow loss 0.051680434
occ loss 0.06879913
time for this batch 0.26595091819763184
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15967552
flow loss 0.05583629
occ loss 0.10305835
time for this batch 0.30368781089782715
----------------------------------
train loss for this epoch:  0.150999
time for this epoch 35.658263206481934
No_decrease:  22
----------------an epoch starts-------------------
i_epoch:  173
# batch:  96
i_batch:  0.0
the loss for this batch:  0.15796532
flow loss 0.054670565
occ loss 0.1026516
time for this batch 0.27045655250549316
----------------------------------
i_batch:  64.0
the loss for this batch:  0.17312184
flow loss 0.057314035
occ loss 0.11536534
time for this batch 0.3030970096588135
----------------------------------
train loss for this epoch:  0.150938
time for this epoch 35.85626769065857
No_decrease:  23
----------------an epoch starts-------------------
i_epoch:  174
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16659436
flow loss 0.058670245
occ loss 0.10717036
time for this batch 0.2614014148712158
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13567214
flow loss 0.04927437
occ loss 0.08596507
time for this batch 0.30046916007995605
----------------------------------
train loss for this epoch:  0.150973
time for this epoch 36.26486897468567
No_decrease:  24
----------------an epoch starts-------------------
i_epoch:  175
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13541023
flow loss 0.046839867
occ loss 0.08806056
time for this batch 0.24441242218017578
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13458592
flow loss 0.05070469
occ loss 0.08329645
time for this batch 0.299288272857666
----------------------------------
train loss for this epoch:  0.15094
time for this epoch 35.81133222579956
No_decrease:  25
----------------an epoch starts-------------------
i_epoch:  176
# batch:  96
i_batch:  0.0
the loss for this batch:  0.17525609
flow loss 0.059762873
occ loss 0.11480266
time for this batch 0.26750683784484863
----------------------------------
i_batch:  64.0
the loss for this batch:  0.1493042
flow loss 0.057168875
occ loss 0.09157919
time for this batch 0.2724144458770752
----------------------------------
train loss for this epoch:  0.150742
time for this epoch 35.885584354400635
No_decrease:  26
----------------an epoch starts-------------------
i_epoch:  177
# batch:  96
i_batch:  0.0
the loss for this batch:  0.11722743
flow loss 0.04808286
occ loss 0.06877148
time for this batch 0.26015424728393555
----------------------------------
i_batch:  64.0
the loss for this batch:  0.15029721
flow loss 0.055439692
occ loss 0.09405122
time for this batch 0.29036831855773926
----------------------------------
train loss for this epoch:  0.150929
time for this epoch 35.31740617752075
No_decrease:  27
----------------an epoch starts-------------------
i_epoch:  178
# batch:  96
i_batch:  0.0
the loss for this batch:  0.16793163
flow loss 0.06142454
occ loss 0.10597673
time for this batch 0.2633180618286133
----------------------------------
i_batch:  64.0
the loss for this batch:  0.18033652
flow loss 0.061378263
occ loss 0.11846303
time for this batch 0.2859475612640381
----------------------------------
train loss for this epoch:  0.151056
time for this epoch 35.67820692062378
No_decrease:  28
----------------an epoch starts-------------------
i_epoch:  179
# batch:  96
i_batch:  0.0
the loss for this batch:  0.13348942
flow loss 0.053044997
occ loss 0.08000504
time for this batch 0.25675511360168457
----------------------------------
i_batch:  64.0
the loss for this batch:  0.116783105
flow loss 0.046292037
occ loss 0.070004895
time for this batch 0.262432336807251
----------------------------------
train loss for this epoch:  0.150631
time for this epoch 35.55372667312622
No_decrease:  29
----------------an epoch starts-------------------
i_epoch:  180
# batch:  96
i_batch:  0.0
the loss for this batch:  0.18282905
flow loss 0.06148705
occ loss 0.12053191
time for this batch 0.2301464080810547
----------------------------------
i_batch:  64.0
the loss for this batch:  0.13023412
flow loss 0.04752938
occ loss 0.08212631
time for this batch 0.29033350944519043
----------------------------------
train loss for this epoch:  0.150831
time for this epoch 35.47727584838867
Early stop at the 181-th epoch

7: apply the model to vali and test¶

In [13]:
def apply_to_vali_test(model, vt, f_o_mean_std):
    f = vt["flow"]
    f_m = vt["flow_mask"].to(device)
    o = vt["occupancy"]
    o_m = vt["occupancy_mask"].to(device)
    f_mae, f_rmse, o_mae, o_rmse  = vali_test(model, f, f_m, o, o_m, f_o_mean_std, hyper["b_s_vt"])
    print ("flow_mae", f_mae)
    print ("flow_rmse", f_rmse)
    print ("occ_mae", o_mae)
    print ("occ_rmse", o_rmse)
    return f_mae, f_rmse, o_mae, o_rmse

Validate¶

In [14]:
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
    apply_to_vali_test(trained_model, vali, f_o_mean_std)
flow_mae 43.11435771146357
flow_rmse 70.86455060162734
occ_mae 0.03549291309592307
occ_rmse 0.06980494802059126

Test¶

In [15]:
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
    apply_to_vali_test(trained_model, test, f_o_mean_std)
flow_mae 41.73699983552198
flow_rmse 68.00268418318444
occ_mae 0.031370341692424086
occ_rmse 0.06279942750383234
In [ ]:
 
In [ ]:
 
In [ ]: